{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "西瓜书习题3.3:编程实现对率回归，并给出西瓜数据集3.0a上的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from numpy import linalg\n",
    "data = np.array([[0.697, 0.774, 0.634, 0.608, 0.556, 0.403, 0.481, 0.437, 0.666, 0.243, 0.245, 0.343, 0.639, 0.657, 0.360, 0.593, 0.719],\n",
    "       [0.460, 0.376, 0.264, 0.318, 0.215, 0.237, 0.149, 0.211, 0.091, 0.267, 0.057, 0.099, 0.161, 0.198, 0.370, 0.042, 0.103],\n",
    "        [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]])\n",
    "label=np.array([1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#一行包含一个样例（x；1），程序中w为（w；b）\n",
    "data =data.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "代价函数：西瓜书公式3.27\n",
    "\n",
    "@para   data：数据集\n",
    "@para   label：标签\n",
    "@para   w：权值加上偏置（w；b）\n",
    "@return loat：当前权值输入下计算得到的误差损失\n",
    "'''\n",
    "def cost_f(data,label,w):\n",
    "    lost = 0.0\n",
    "    for i in range(data.shape[0]):\n",
    "        x = data[i]\n",
    "        lost = lost+ (-label[i] * np.dot(w.T,x) + np.log(1 + np.exp(np.dot(w.T,x)))) \n",
    "    return lost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "牛顿法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "西瓜书公式3.23，计算后验概率估计p（y=1|x）\n",
    "\n",
    "@para   x：数据集\n",
    "@para   w：权值加上偏置（w；b）\n",
    "@return p1\n",
    "'''\n",
    "def p1(x, w):\n",
    "    temp = np.exp(np.dot(w.T,x))\n",
    "    return temp/(1+temp)\n",
    "'''\n",
    "#西瓜书公式3.30，计算损失函数的一阶导\n",
    "\n",
    "@para   data：数据集\n",
    "@para   label：标签\n",
    "@para   w：权值加上偏置（w；b）\n",
    "@return w_1d：损失函数的一阶导\n",
    "'''\n",
    "def cost_f_1differential(w, data, label):\n",
    "    w_1d = np.array(np.zeros(data[0].shape))\n",
    "    for i in range(data.shape[0]):  \n",
    "        x = data[i]\n",
    "        y = label[i]\n",
    "        w_1d += -np.dot(x, (y - p1(x, w)))\n",
    "    return w_1d;\n",
    "'''\n",
    "#西瓜书公式3.31，计算损失函数的二阶导\n",
    "\n",
    "@para   data：数据集\n",
    "@para   w：权值加上偏置（w；b）\n",
    "@return w_2d：损失函数的二阶导\n",
    "'''\n",
    "def cost_f_2differential(w, data):\n",
    "    w_2d = 0.0\n",
    "    for i in range(data.shape[0]):\n",
    "        x = data[i]\n",
    "        w_2d += np.dot(x,x.T)*(1 - p1(x, w))*p1(x, w)\n",
    "    return w_2d;\n",
    "'''\n",
    "#牛顿法迭代计算似然函数最小值\n",
    "\n",
    "@para   data：数据集\n",
    "@para   label：标签\n",
    "@para   w：权值加上偏置（w；b）\n",
    "@return w：更新后的w\n",
    "'''\n",
    "def newton_method(w, data, label):\n",
    "    cost_last = 0\n",
    "    cost= 1\n",
    "    i =0\n",
    "    while(np.abs(cost-cost_last)>0.00001):\n",
    "        cost_last = cost\n",
    "        w_1d = cost_f_1differential(w, data, label)\n",
    "        w_2d = cost_f_2differential(w, data)\n",
    "        w = w - w_1d/w_2d\n",
    "        cost = cost_f(data,label,w)\n",
    "        i=i+1\n",
    "        print('period:',i,' cost is:',cost)\n",
    "    return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#初始化w，表示（w；b），在西瓜数据集3.0中为三位\n",
    "w0 = np.array(np.zeros(data[0].shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "period: 1  cost is: 11.718483841701428\n",
      "period: 2  cost is: 11.663885021597467\n",
      "period: 3  cost is: 11.61052579882915\n",
      "period: 4  cost is: 11.55836950185784\n",
      "period: 5  cost is: 11.507383257450465\n",
      "period: 6  cost is: 11.457535132822368\n",
      "period: 7  cost is: 11.408794165275152\n",
      "period: 8  cost is: 11.361130334893245\n",
      "period: 9  cost is: 11.314514536400706\n",
      "period: 10  cost is: 11.268918551564868\n",
      "period: 11  cost is: 11.224315022197851\n",
      "period: 12  cost is: 11.180677423771886\n",
      "period: 13  cost is: 11.13798003965988\n",
      "period: 14  cost is: 11.096197936009235\n",
      "period: 15  cost is: 11.055306937253766\n",
      "period: 16  cost is: 11.015283602265885\n",
      "period: 17  cost is: 10.976105201148819\n",
      "period: 18  cost is: 10.937749692666438\n",
      "period: 19  cost is: 10.900195702306473\n",
      "period: 20  cost is: 10.863422500971273\n",
      "period: 21  cost is: 10.82740998428871\n",
      "period: 22  cost is: 10.792138652534886\n",
      "period: 23  cost is: 10.757589591158965\n",
      "period: 24  cost is: 10.723744451899805\n",
      "period: 25  cost is: 10.6905854344832\n",
      "period: 26  cost is: 10.658095268888006\n",
      "period: 27  cost is: 10.626257198168894\n",
      "period: 28  cost is: 10.595054961823084\n",
      "period: 29  cost is: 10.564472779688236\n",
      "period: 30  cost is: 10.534495336358217\n",
      "period: 31  cost is: 10.505107766103635\n",
      "period: 32  cost is: 10.476295638283673\n",
      "period: 33  cost is: 10.44804494323595\n",
      "period: 34  cost is: 10.420342078631016\n",
      "period: 35  cost is: 10.393173836278285\n",
      "period: 36  cost is: 10.366527389370216\n",
      "period: 37  cost is: 10.340390280151762\n",
      "period: 38  cost is: 10.314750408002313\n",
      "period: 39  cost is: 10.28959601791737\n",
      "period: 40  cost is: 10.264915689377727\n",
      "period: 41  cost is: 10.240698325593819\n",
      "period: 42  cost is: 10.216933143113385\n",
      "period: 43  cost is: 10.19360966178079\n",
      "period: 44  cost is: 10.170717695036494\n",
      "period: 45  cost is: 10.148247340545693\n",
      "period: 46  cost is: 10.126188971145135\n",
      "period: 47  cost is: 10.10453322609761\n",
      "period: 48  cost is: 10.083271002643814\n",
      "period: 49  cost is: 10.062393447841576\n",
      "period: 50  cost is: 10.041891950682711\n",
      "period: 51  cost is: 10.021758134478084\n",
      "period: 52  cost is: 10.001983849501709\n",
      "period: 53  cost is: 9.982561165884986\n",
      "period: 54  cost is: 9.96348236675249\n",
      "period: 55  cost is: 9.944739941590939\n",
      "period: 56  cost is: 9.92632657984328\n",
      "period: 57  cost is: 9.908235164720054\n",
      "period: 58  cost is: 9.89045876722049\n",
      "period: 59  cost is: 9.872990640355972\n",
      "period: 60  cost is: 9.855824213568859\n",
      "period: 61  cost is: 9.838953087339732\n",
      "period: 62  cost is: 9.82237102797655\n",
      "period: 63  cost is: 9.806071962579214\n",
      "period: 64  cost is: 9.79004997417349\n",
      "period: 65  cost is: 9.77429929700824\n",
      "period: 66  cost is: 9.758814312010218\n",
      "period: 67  cost is: 9.743589542390952\n",
      "period: 68  cost is: 9.728619649400231\n",
      "period: 69  cost is: 9.713899428221154\n",
      "period: 70  cost is: 9.699423804001615\n",
      "period: 71  cost is: 9.685187828017565\n",
      "period: 72  cost is: 9.671186673963224\n",
      "period: 73  cost is: 9.657415634363977\n",
      "period: 74  cost is: 9.643870117107413\n",
      "period: 75  cost is: 9.63054564208853\n",
      "period: 76  cost is: 9.61743783796495\n",
      "period: 77  cost is: 9.604542439018406\n",
      "period: 78  cost is: 9.591855282118615\n",
      "period: 79  cost is: 9.579372303786078\n",
      "period: 80  cost is: 9.567089537350263\n",
      "period: 81  cost is: 9.555003110199827\n",
      "period: 82  cost is: 9.543109241121707\n",
      "period: 83  cost is: 9.531404237725923\n",
      "period: 84  cost is: 9.519884493953096\n",
      "period: 85  cost is: 9.508546487661864\n",
      "period: 86  cost is: 9.49738677829332\n",
      "period: 87  cost is: 9.486402004609875\n",
      "period: 88  cost is: 9.475588882505905\n",
      "period: 89  cost is: 9.464944202887736\n",
      "period: 90  cost is: 9.45446482962053\n",
      "period: 91  cost is: 9.4441476975398\n",
      "period: 92  cost is: 9.433989810525283\n",
      "period: 93  cost is: 9.423988239635069\n",
      "period: 94  cost is: 9.414140121297862\n",
      "period: 95  cost is: 9.404442655561452\n",
      "period: 96  cost is: 9.394893104395393\n",
      "period: 97  cost is: 9.385488790046097\n",
      "period: 98  cost is: 9.37622709344253\n",
      "period: 99  cost is: 9.367105452650797\n",
      "period: 100  cost is: 9.358121361375945\n",
      "period: 101  cost is: 9.349272367509394\n",
      "period: 102  cost is: 9.340556071720478\n",
      "period: 103  cost is: 9.331970126090535\n",
      "period: 104  cost is: 9.323512232788215\n",
      "period: 105  cost is: 9.315180142784545\n",
      "period: 106  cost is: 9.306971654606436\n",
      "period: 107  cost is: 9.298884613127397\n",
      "period: 108  cost is: 9.290916908394127\n",
      "period: 109  cost is: 9.283066474487885\n",
      "period: 110  cost is: 9.275331288419427\n",
      "period: 111  cost is: 9.267709369056407\n",
      "period: 112  cost is: 9.260198776082193\n",
      "period: 113  cost is: 9.252797608985064\n",
      "period: 114  cost is: 9.245504006076732\n",
      "period: 115  cost is: 9.238316143539341\n",
      "period: 116  cost is: 9.231232234499904\n",
      "period: 117  cost is: 9.224250528131337\n",
      "period: 118  cost is: 9.217369308779244\n",
      "period: 119  cost is: 9.210586895113565\n",
      "period: 120  cost is: 9.203901639304343\n",
      "period: 121  cost is: 9.197311926220786\n",
      "period: 122  cost is: 9.190816172652921\n",
      "period: 123  cost is: 9.184412826555064\n",
      "period: 124  cost is: 9.178100366310476\n",
      "period: 125  cost is: 9.171877300016455\n",
      "period: 126  cost is: 9.1657421647893\n",
      "period: 127  cost is: 9.159693526088443\n",
      "period: 128  cost is: 9.153729977059196\n",
      "period: 129  cost is: 9.147850137893501\n",
      "period: 130  cost is: 9.142052655208142\n",
      "period: 131  cost is: 9.136336201439828\n",
      "period: 132  cost is: 9.130699474256714\n",
      "period: 133  cost is: 9.125141195985716\n",
      "period: 134  cost is: 9.119660113055279\n",
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      "period: 136  cost is: 9.108924636197715\n",
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      "period: 161  cost is: 8.996848093985953\n",
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      "period: 239  cost is: 8.810821981405116\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "period: 240  cost is: 8.80941272926586\n",
      "period: 241  cost is: 8.808020183175966\n",
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      "period: 628  cost is: 8.686044117164794\n",
      "period: 629  cost is: 8.686020977851554\n",
      "period: 630  cost is: 8.685998065682035\n",
      "period: 631  cost is: 8.685975378389976\n",
      "period: 632  cost is: 8.68595291373226\n",
      "period: 633  cost is: 8.685930669488664\n",
      "period: 634  cost is: 8.685908643461632\n",
      "period: 635  cost is: 8.685886833476033\n",
      "period: 636  cost is: 8.685865237378913\n",
      "period: 637  cost is: 8.685843853039259\n",
      "period: 638  cost is: 8.68582267834781\n",
      "period: 639  cost is: 8.685801711216765\n",
      "period: 640  cost is: 8.685780949579604\n",
      "period: 641  cost is: 8.685760391390849\n",
      "period: 642  cost is: 8.685740034625834\n",
      "period: 643  cost is: 8.685719877280503\n",
      "period: 644  cost is: 8.685699917371185\n",
      "period: 645  cost is: 8.68568015293438\n",
      "period: 646  cost is: 8.685660582026545\n",
      "period: 647  cost is: 8.685641202723893\n",
      "period: 648  cost is: 8.685622013122176\n",
      "period: 649  cost is: 8.685603011336497\n",
      "period: 650  cost is: 8.685584195501077\n",
      "period: 651  cost is: 8.685565563769094\n",
      "period: 652  cost is: 8.685547114312453\n",
      "period: 653  cost is: 8.685528845321594\n",
      "period: 654  cost is: 8.685510755005328\n",
      "period: 655  cost is: 8.685492841590603\n",
      "period: 656  cost is: 8.685475103322343\n",
      "period: 657  cost is: 8.685457538463254\n",
      "period: 658  cost is: 8.685440145293631\n",
      "period: 659  cost is: 8.685422922111185\n",
      "period: 660  cost is: 8.685405867230855\n",
      "period: 661  cost is: 8.685388978984623\n",
      "period: 662  cost is: 8.685372255721356\n",
      "period: 663  cost is: 8.685355695806606\n",
      "period: 664  cost is: 8.68533929762246\n",
      "period: 665  cost is: 8.685323059567345\n",
      "period: 666  cost is: 8.685306980055875\n",
      "period: 667  cost is: 8.685291057518677\n",
      "period: 668  cost is: 8.68527529040222\n",
      "period: 669  cost is: 8.685259677168661\n",
      "period: 670  cost is: 8.685244216295672\n",
      "period: 671  cost is: 8.685228906276285\n",
      "period: 672  cost is: 8.685213745618725\n",
      "period: 673  cost is: 8.685198732846274\n",
      "period: 674  cost is: 8.68518386649708\n",
      "period: 675  cost is: 8.68516914512404\n",
      "period: 676  cost is: 8.68515456729461\n",
      "period: 677  cost is: 8.685140131590693\n",
      "period: 678  cost is: 8.68512583660846\n",
      "period: 679  cost is: 8.685111680958215\n",
      "period: 680  cost is: 8.685097663264244\n",
      "period: 681  cost is: 8.685083782164678\n",
      "period: 682  cost is: 8.685070036311341\n",
      "period: 683  cost is: 8.685056424369618\n",
      "period: 684  cost is: 8.685042945018303\n",
      "period: 685  cost is: 8.68502959694947\n",
      "period: 686  cost is: 8.685016378868335\n",
      "period: 687  cost is: 8.685003289493118\n",
      "period: 688  cost is: 8.684990327554909\n",
      "period: 689  cost is: 8.684977491797536\n",
      "period: 690  cost is: 8.684964780977438\n",
      "period: 691  cost is: 8.68495219386352\n",
      "period: 692  cost is: 8.684939729237053\n",
      "period: 693  cost is: 8.684927385891513\n",
      "period: 694  cost is: 8.684915162632478\n",
      "period: 695  cost is: 8.684903058277511\n",
      "period: 696  cost is: 8.684891071656\n",
      "period: 697  cost is: 8.684879201609075\n",
      "period: 698  cost is: 8.684867446989466\n",
      "period: 699  cost is: 8.684855806661398\n",
      "period: 700  cost is: 8.684844279500462\n",
      "period: 701  cost is: 8.684832864393494\n",
      "period: 702  cost is: 8.684821560238477\n",
      "period: 703  cost is: 8.684810365944418\n",
      "period: 704  cost is: 8.684799280431225\n",
      "period: 705  cost is: 8.684788302629611\n",
      "period: 706  cost is: 8.684777431480981\n",
      "period: 707  cost is: 8.68476666593731\n",
      "period: 708  cost is: 8.684756004961047\n",
      "period: 709  cost is: 8.684745447525005\n",
      "period: 710  cost is: 8.684734992612253\n",
      "period: 711  cost is: 8.684724639216011\n",
      "period: 712  cost is: 8.684714386339554\n",
      "period: 713  cost is: 8.684704232996094\n",
      "period: 714  cost is: 8.68469417820869\n",
      "period: 715  cost is: 8.684684221010153\n"
     ]
    }
   ],
   "source": [
    "#牛顿法计算，返回最优解\n",
    "w = newton_method(w0, data, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图显示最终结果\n",
    "import matplotlib.pyplot as plt\n",
    "for i in range(data.shape[0]):\n",
    "    if(label[i]==0):\n",
    "        plt.plot(data[i][0],data[i][1],'r+')\n",
    "    else:\n",
    "        plt.plot(data[i][0],data[i][1],'bo')\n",
    "x = np.linspace(0, 1, 50)\n",
    "y = -w[0]/w[1]*x - w[2]/w[1]\n",
    "plt.plot(x,y,'y-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SGD：随机梯度下降"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "梯度下降更新w\n",
    "\n",
    "@para   data：数据集\n",
    "@para   label：标签\n",
    "@para   w：权值加上偏置（w；b）\n",
    "@para   period：周期\n",
    "@return w：更新后的w\n",
    "'''\n",
    "def GD(w, data, label,period):\n",
    "    a = 0.001   #学习率\n",
    "    cost_last = 0\n",
    "    for i in range(period):\n",
    "        w = w - a * cost_f_1differential(w, data, label)\n",
    "        cost = cost_f(data,label,w)\n",
    "        print('period:',i,' cost is:',cost)\n",
    "        if(np.abs(cost-cost_last)<0.00001):\n",
    "            return w\n",
    "    return w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "period: 0  cost is: 11.783071230195219\n",
      "period: 1  cost is: 11.78264161583686\n",
      "period: 2  cost is: 11.78221321306117\n",
      "period: 3  cost is: 11.781786008636665\n",
      "period: 4  cost is: 11.781359989481548\n",
      "period: 5  cost is: 11.780935142661956\n",
      "period: 6  cost is: 11.780511455390338\n",
      "period: 7  cost is: 11.780088915023768\n",
      "period: 8  cost is: 11.779667509062346\n",
      "period: 9  cost is: 11.779247225147543\n",
      "period: 10  cost is: 11.77882805106063\n",
      "period: 11  cost is: 11.778409974721095\n",
      "period: 12  cost is: 11.77799298418507\n",
      "period: 13  cost is: 11.77757706764381\n",
      "period: 14  cost is: 11.777162213422129\n",
      "period: 15  cost is: 11.77674840997693\n",
      "period: 16  cost is: 11.776335645895688\n",
      "period: 17  cost is: 11.775923909894981\n",
      "period: 18  cost is: 11.775513190819028\n",
      "period: 19  cost is: 11.775103477638256\n",
      "period: 20  cost is: 11.774694759447858\n",
      "period: 21  cost is: 11.774287025466398\n",
      "period: 22  cost is: 11.773880265034405\n",
      "period: 23  cost is: 11.773474467613006\n",
      "period: 24  cost is: 11.773069622782543\n",
      "period: 25  cost is: 11.772665720241246\n",
      "period: 26  cost is: 11.772262749803886\n",
      "period: 27  cost is: 11.771860701400476\n",
      "period: 28  cost is: 11.771459565074938\n",
      "period: 29  cost is: 11.771059330983835\n",
      "period: 30  cost is: 11.770659989395106\n",
      "period: 31  cost is: 11.770261530686783\n",
      "period: 32  cost is: 11.769863945345762\n",
      "period: 33  cost is: 11.769467223966565\n",
      "period: 34  cost is: 11.769071357250125\n",
      "period: 35  cost is: 11.768676336002589\n",
      "period: 36  cost is: 11.768282151134112\n",
      "period: 37  cost is: 11.767888793657697\n",
      "period: 38  cost is: 11.767496254688027\n",
      "period: 39  cost is: 11.7671045254403\n",
      "period: 40  cost is: 11.766713597229128\n",
      "period: 41  cost is: 11.76632346146736\n",
      "period: 42  cost is: 11.765934109665025\n",
      "period: 43  cost is: 11.765545533428192\n",
      "period: 44  cost is: 11.76515772445791\n",
      "period: 45  cost is: 11.764770674549116\n",
      "period: 46  cost is: 11.764384375589579\n",
      "period: 47  cost is: 11.763998819558854\n",
      "period: 48  cost is: 11.763613998527235\n",
      "period: 49  cost is: 11.763229904654736\n",
      "period: 50  cost is: 11.76284653019008\n",
      "period: 51  cost is: 11.76246386746967\n",
      "period: 52  cost is: 11.762081908916631\n",
      "period: 53  cost is: 11.7617006470398\n",
      "period: 54  cost is: 11.761320074432783\n",
      "period: 55  cost is: 11.760940183772966\n",
      "period: 56  cost is: 11.760560967820599\n",
      "period: 57  cost is: 11.760182419417829\n",
      "period: 58  cost is: 11.759804531487784\n",
      "period: 59  cost is: 11.759427297033671\n",
      "period: 60  cost is: 11.759050709137844\n",
      "period: 61  cost is: 11.758674760960925\n",
      "period: 62  cost is: 11.75829944574092\n",
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      "period: 64  cost is: 11.757550687505304\n",
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      "period: 77  cost is: 11.752741300968472\n",
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      "period: 84  cost is: 11.750189886148627\n",
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      "period: 87  cost is: 11.749103982241632\n",
      "period: 88  cost is: 11.74874298425472\n",
      "period: 89  cost is: 11.748382462922043\n",
      "period: 90  cost is: 11.748022413326273\n",
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      "period: 1986  cost is: 11.203718070796253\n",
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      "period: 1988  cost is: 11.203215328321544\n",
      "period: 1989  cost is: 11.202964005907791\n",
      "period: 1990  cost is: 11.202712716036658\n",
      "period: 1991  cost is: 11.202461458703295\n",
      "period: 1992  cost is: 11.202210233902877\n",
      "period: 1993  cost is: 11.20195904163056\n",
      "period: 1994  cost is: 11.201707881881505\n",
      "period: 1995  cost is: 11.201456754650879\n",
      "period: 1996  cost is: 11.201205659933846\n",
      "period: 1997  cost is: 11.200954597725572\n",
      "period: 1998  cost is: 11.200703568021222\n",
      "period: 1999  cost is: 11.200452570815955\n",
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      "period: 2018  cost is: 11.195689792319246\n",
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      "period: 8915  cost is: 10.0317008270564\n",
      "period: 8916  cost is: 10.0315904539989\n",
      "period: 8917  cost is: 10.031480092805866\n",
      "period: 8918  cost is: 10.031369743475675\n",
      "period: 8919  cost is: 10.031259406006704\n",
      "period: 8920  cost is: 10.031149080397327\n",
      "period: 8921  cost is: 10.031038766645935\n",
      "period: 8922  cost is: 10.030928464750893\n",
      "period: 8923  cost is: 10.030818174710584\n",
      "period: 8924  cost is: 10.030707896523396\n",
      "period: 8925  cost is: 10.030597630187694\n",
      "period: 8926  cost is: 10.03048737570187\n",
      "period: 8927  cost is: 10.0303771330643\n",
      "period: 8928  cost is: 10.030266902273365\n",
      "period: 8929  cost is: 10.03015668332744\n",
      "period: 8930  cost is: 10.03004647622492\n",
      "period: 8931  cost is: 10.029936280964172\n",
      "period: 8932  cost is: 10.02982609754358\n",
      "period: 8933  cost is: 10.029715925961531\n",
      "period: 8934  cost is: 10.029605766216406\n",
      "period: 8935  cost is: 10.029495618306585\n",
      "period: 8936  cost is: 10.029385482230452\n",
      "period: 8937  cost is: 10.029275357986384\n",
      "period: 8938  cost is: 10.029165245572774\n",
      "period: 8939  cost is: 10.029055144988\n",
      "period: 8940  cost is: 10.028945056230448\n",
      "period: 8941  cost is: 10.028834979298496\n",
      "period: 8942  cost is: 10.028724914190532\n",
      "period: 8943  cost is: 10.028614860904941\n",
      "period: 8944  cost is: 10.028504819440109\n",
      "period: 8945  cost is: 10.028394789794415\n",
      "period: 8946  cost is: 10.028284771966254\n",
      "period: 8947  cost is: 10.028174765954\n",
      "period: 8948  cost is: 10.028064771756046\n",
      "period: 8949  cost is: 10.027954789370778\n",
      "period: 8950  cost is: 10.027844818796577\n",
      "period: 8951  cost is: 10.027734860031831\n",
      "period: 8952  cost is: 10.02762491307493\n",
      "period: 8953  cost is: 10.027514977924262\n",
      "period: 8954  cost is: 10.02740505457821\n",
      "period: 8955  cost is: 10.027295143035161\n",
      "period: 8956  cost is: 10.027185243293506\n",
      "period: 8957  cost is: 10.027075355351634\n",
      "period: 8958  cost is: 10.026965479207929\n",
      "period: 8959  cost is: 10.026855614860784\n",
      "period: 8960  cost is: 10.026745762308582\n",
      "period: 8961  cost is: 10.026635921549715\n",
      "period: 8962  cost is: 10.026526092582577\n",
      "period: 8963  cost is: 10.026416275405548\n",
      "period: 8964  cost is: 10.02630647001703\n",
      "period: 8965  cost is: 10.026196676415402\n",
      "period: 8966  cost is: 10.026086894599057\n",
      "period: 8967  cost is: 10.025977124566396\n",
      "period: 8968  cost is: 10.025867366315794\n",
      "period: 8969  cost is: 10.025757619845647\n",
      "period: 8970  cost is: 10.025647885154354\n",
      "period: 8971  cost is: 10.0255381622403\n",
      "period: 8972  cost is: 10.02542845110188\n",
      "period: 8973  cost is: 10.02531875173748\n",
      "period: 8974  cost is: 10.025209064145498\n",
      "period: 8975  cost is: 10.025099388324328\n",
      "period: 8976  cost is: 10.02498972427236\n",
      "period: 8977  cost is: 10.024880071987988\n",
      "period: 8978  cost is: 10.024770431469605\n",
      "period: 8979  cost is: 10.024660802715609\n",
      "period: 8980  cost is: 10.024551185724384\n",
      "period: 8981  cost is: 10.024441580494335\n",
      "period: 8982  cost is: 10.02433198702385\n",
      "period: 8983  cost is: 10.024222405311322\n",
      "period: 8984  cost is: 10.024112835355156\n",
      "period: 8985  cost is: 10.024003277153739\n",
      "period: 8986  cost is: 10.023893730705469\n",
      "period: 8987  cost is: 10.023784196008739\n",
      "period: 8988  cost is: 10.023674673061949\n",
      "period: 8989  cost is: 10.023565161863493\n",
      "period: 8990  cost is: 10.023455662411768\n",
      "period: 8991  cost is: 10.023346174705175\n",
      "period: 8992  cost is: 10.023236698742105\n",
      "period: 8993  cost is: 10.023127234520961\n",
      "period: 8994  cost is: 10.023017782040132\n",
      "period: 8995  cost is: 10.022908341298024\n",
      "period: 8996  cost is: 10.022798912293032\n",
      "period: 8997  cost is: 10.022689495023558\n",
      "period: 8998  cost is: 10.022580089487994\n",
      "period: 8999  cost is: 10.022470695684744\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图显示最终结果\n",
    "period = 9000\n",
    "w2 = GD(w0, data, label,period)\n",
    "import matplotlib.pyplot as plt\n",
    "for i in range(data.shape[0]):\n",
    "    if(label[i]==0):\n",
    "        plt.plot(data[i][0],data[i][1],'r+')\n",
    "    else:\n",
    "        plt.plot(data[i][0],data[i][1],'bo')\n",
    "x = np.linspace(0, 1, 50)\n",
    "y1 = -w[0]/w[1]*x - w[2]/w[1]\n",
    "y2 = -w2[0]/w2[1]*x - w2[2]/w2[1]\n",
    "plt.plot(x,y,'y-')\n",
    "plt.plot(x,y2,'g-')\n",
    "plt.show()"
   ]
  }
 ],
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